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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'object', 'count'}) and 3 missing columns ({'object2', 'distance', 'object1'}).
This happened while the json dataset builder was generating data using
hf://datasets/OPPOer/VSI-100k/object_counting.json (at revision b1d31dedc3b4a81575c3361fbb2cfe852fa5e9b3)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
object: string
count: int64
type: string
scene_name: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 558
to
{'object1': Value(dtype='string', id=None), 'object2': Value(dtype='string', id=None), 'distance': Value(dtype='float64', id=None), 'type': Value(dtype='string', id=None), 'scene_name': Value(dtype='string', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1436, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1053, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'object', 'count'}) and 3 missing columns ({'object2', 'distance', 'object1'}).
This happened while the json dataset builder was generating data using
hf://datasets/OPPOer/VSI-100k/object_counting.json (at revision b1d31dedc3b4a81575c3361fbb2cfe852fa5e9b3)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
object1
string | object2
string | distance
float64 | type
string | scene_name
string |
|---|---|---|---|---|
shower
|
kitchen counter
| 6.081789 |
absolute_distance
|
scene0000_00
|
desk
|
kitchen counter
| 7.836994 |
absolute_distance
|
scene0000_00
|
sink
|
kitchen counter
| 4.986654 |
absolute_distance
|
scene0000_00
|
kitchen counter
|
tv
| 5.585205 |
absolute_distance
|
scene0000_00
|
pillow
|
kitchen counter
| 6.869213 |
absolute_distance
|
scene0000_00
|
backpack
|
kitchen counter
| 5.340563 |
absolute_distance
|
scene0000_00
|
couch
|
kitchen counter
| 3.544374 |
absolute_distance
|
scene0000_00
|
refrigerator
|
kitchen counter
| 1.644769 |
absolute_distance
|
scene0000_00
|
coffee table
|
kitchen counter
| 3.897781 |
absolute_distance
|
scene0000_00
|
toilet
|
kitchen counter
| 5.873805 |
absolute_distance
|
scene0000_00
|
kitchen counter
|
bed
| 5.99473 |
absolute_distance
|
scene0000_00
|
laundry basket
|
kitchen counter
| 5.907288 |
absolute_distance
|
scene0000_00
|
guitar
|
kitchen counter
| 4.759872 |
absolute_distance
|
scene0000_00
|
dish rack
|
kitchen counter
| 0.203097 |
absolute_distance
|
scene0000_00
|
kitchen counter
|
shelf
| 3.648144 |
absolute_distance
|
scene0000_00
|
kitchen counter
|
bicycle
| 6.432842 |
absolute_distance
|
scene0000_00
|
shower
|
desk
| 3.672814 |
absolute_distance
|
scene0000_00
|
sink
|
shower
| 1.90053 |
absolute_distance
|
scene0000_00
|
shower
|
tv
| 6.871887 |
absolute_distance
|
scene0000_00
|
pillow
|
shower
| 1.74964 |
absolute_distance
|
scene0000_00
|
shower
|
backpack
| 4.837669 |
absolute_distance
|
scene0000_00
|
couch
|
shower
| 4.648982 |
absolute_distance
|
scene0000_00
|
refrigerator
|
shower
| 4.447896 |
absolute_distance
|
scene0000_00
|
coffee table
|
shower
| 5.692396 |
absolute_distance
|
scene0000_00
|
toilet
|
shower
| 1.609814 |
absolute_distance
|
scene0000_00
|
bed
|
shower
| 1.731755 |
absolute_distance
|
scene0000_00
|
shower
|
laundry basket
| 0.906089 |
absolute_distance
|
scene0000_00
|
guitar
|
shower
| 1.551059 |
absolute_distance
|
scene0000_00
|
dish rack
|
shower
| 6.198181 |
absolute_distance
|
scene0000_00
|
shelf
|
shower
| 6.48553 |
absolute_distance
|
scene0000_00
|
bicycle
|
shower
| 6.284457 |
absolute_distance
|
scene0000_00
|
desk
|
sink
| 5.395177 |
absolute_distance
|
scene0000_00
|
tv
|
desk
| 5.562157 |
absolute_distance
|
scene0000_00
|
pillow
|
desk
| 1.999431 |
absolute_distance
|
scene0000_00
|
backpack
|
desk
| 3.56789 |
absolute_distance
|
scene0000_00
|
desk
|
couch
| 4.765699 |
absolute_distance
|
scene0000_00
|
desk
|
refrigerator
| 6.483336 |
absolute_distance
|
scene0000_00
|
desk
|
coffee table
| 5.364831 |
absolute_distance
|
scene0000_00
|
desk
|
toilet
| 5.22819 |
absolute_distance
|
scene0000_00
|
desk
|
bed
| 2.242817 |
absolute_distance
|
scene0000_00
|
desk
|
laundry basket
| 3.02629 |
absolute_distance
|
scene0000_00
|
desk
|
guitar
| 3.711609 |
absolute_distance
|
scene0000_00
|
desk
|
dish rack
| 7.990178 |
absolute_distance
|
scene0000_00
|
shelf
|
desk
| 6.329207 |
absolute_distance
|
scene0000_00
|
bicycle
|
desk
| 4.291435 |
absolute_distance
|
scene0000_00
|
tv
|
sink
| 7.366371 |
absolute_distance
|
scene0000_00
|
pillow
|
sink
| 3.575043 |
absolute_distance
|
scene0000_00
|
sink
|
backpack
| 5.573814 |
absolute_distance
|
scene0000_00
|
sink
|
couch
| 4.761984 |
absolute_distance
|
scene0000_00
|
refrigerator
|
sink
| 3.417342 |
absolute_distance
|
scene0000_00
|
sink
|
coffee table
| 5.821157 |
absolute_distance
|
scene0000_00
|
sink
|
toilet
| 0.903757 |
absolute_distance
|
scene0000_00
|
sink
|
bed
| 3.203403 |
absolute_distance
|
scene0000_00
|
sink
|
laundry basket
| 2.389633 |
absolute_distance
|
scene0000_00
|
sink
|
guitar
| 2.005964 |
absolute_distance
|
scene0000_00
|
dish rack
|
sink
| 5.076984 |
absolute_distance
|
scene0000_00
|
shelf
|
sink
| 6.454152 |
absolute_distance
|
scene0000_00
|
bicycle
|
sink
| 7.130202 |
absolute_distance
|
scene0000_00
|
tv
|
pillow
| 6.161451 |
absolute_distance
|
scene0000_00
|
backpack
|
tv
| 2.30602 |
absolute_distance
|
scene0000_00
|
tv
|
couch
| 2.743193 |
absolute_distance
|
scene0000_00
|
refrigerator
|
tv
| 5.408658 |
absolute_distance
|
scene0000_00
|
coffee table
|
tv
| 1.969658 |
absolute_distance
|
scene0000_00
|
toilet
|
tv
| 7.894114 |
absolute_distance
|
scene0000_00
|
bed
|
tv
| 5.441078 |
absolute_distance
|
scene0000_00
|
laundry basket
|
tv
| 6.174025 |
absolute_distance
|
scene0000_00
|
tv
|
guitar
| 5.583381 |
absolute_distance
|
scene0000_00
|
dish rack
|
tv
| 5.714525 |
absolute_distance
|
scene0000_00
|
shelf
|
tv
| 2.031261 |
absolute_distance
|
scene0000_00
|
bicycle
|
tv
| 1.595303 |
absolute_distance
|
scene0000_00
|
pillow
|
backpack
| 4.081304 |
absolute_distance
|
scene0000_00
|
pillow
|
couch
| 4.528205 |
absolute_distance
|
scene0000_00
|
refrigerator
|
pillow
| 5.322319 |
absolute_distance
|
scene0000_00
|
coffee table
|
pillow
| 5.432617 |
absolute_distance
|
scene0000_00
|
toilet
|
pillow
| 3.351372 |
absolute_distance
|
scene0000_00
|
pillow
|
bed
| 1.040756 |
absolute_distance
|
scene0000_00
|
laundry basket
|
pillow
| 1.374486 |
absolute_distance
|
scene0000_00
|
guitar
|
pillow
| 2.281445 |
absolute_distance
|
scene0000_00
|
dish rack
|
pillow
| 7.001671 |
absolute_distance
|
scene0000_00
|
pillow
|
shelf
| 6.297645 |
absolute_distance
|
scene0000_00
|
bicycle
|
pillow
| 5.281142 |
absolute_distance
|
scene0000_00
|
couch
|
backpack
| 1.798184 |
absolute_distance
|
scene0000_00
|
backpack
|
refrigerator
| 4.565127 |
absolute_distance
|
scene0000_00
|
backpack
|
coffee table
| 1.909726 |
absolute_distance
|
scene0000_00
|
backpack
|
toilet
| 5.954347 |
absolute_distance
|
scene0000_00
|
backpack
|
bed
| 3.291727 |
absolute_distance
|
scene0000_00
|
laundry basket
|
backpack
| 4.045896 |
absolute_distance
|
scene0000_00
|
backpack
|
guitar
| 3.629262 |
absolute_distance
|
scene0000_00
|
dish rack
|
backpack
| 5.507423 |
absolute_distance
|
scene0000_00
|
backpack
|
shelf
| 2.964102 |
absolute_distance
|
scene0000_00
|
bicycle
|
backpack
| 1.590977 |
absolute_distance
|
scene0000_00
|
couch
|
refrigerator
| 2.891551 |
absolute_distance
|
scene0000_00
|
couch
|
coffee table
| 1.08374 |
absolute_distance
|
scene0000_00
|
couch
|
toilet
| 5.371846 |
absolute_distance
|
scene0000_00
|
couch
|
bed
| 3.594698 |
absolute_distance
|
scene0000_00
|
couch
|
laundry basket
| 4.035637 |
absolute_distance
|
scene0000_00
|
guitar
|
couch
| 3.168138 |
absolute_distance
|
scene0000_00
|
couch
|
dish rack
| 3.709632 |
absolute_distance
|
scene0000_00
|
shelf
|
couch
| 1.956823 |
absolute_distance
|
scene0000_00
|
couch
|
bicycle
| 3.028895 |
absolute_distance
|
scene0000_00
|
End of preview.
Improved Visual-Spatial Reasoning via R1-Zero-Like Training
Zhenyi Liao, Qingsong Xie, Yanhao Zhang, Zijian Kong, Haonan Lu, Zhenyu Yang, Zhijie Deng
📅 News
- 🚀 [06/04/2025] We release VSI-100k.
- 🚀 [04/02/2025] We release our paper on arxiv.
🌞 Highlights
🔔 We identify that the visual-spatial reasoning capacities of small- to medium-sized Qwen2-VL models cannot be activated via Chain of Thought (CoT) prompts.
🔔 We incorporate GRPO training for improved visual-spatial reasoning, using the carefully curated VSI-100k dataset.
🔔 With GRPO training, our vsGRPO-2B outperforms GPT-4o, and the vsGRPO-7B demonstrates performance comparable to the best open-source model, LLaVA-Video-Next-72B.
🤗 VSI-100k
To combat data scarcity, we build VSI-100k. Specifically, with the ScanNet 3D annotation information, we construct approximately 100k question-answer pairs for the training.
Here we release the raw data for the community. Specifically, we split the question types into six categories:
We are releasing the raw data for the community. The question types have been categorized into seven distinct categories:
- Absolute Distance: Given two unique objects in the scene, we provide the distance in meters between them.
- Object Counting: The total number of objects present in the entire scene.
- Object Size: The three dimensions of a unique object within the scene.
- Relative Direction: Given the location of the observer and their viewpoint, we provide the relative direction of the target concerning the observer. Note that there are three types of answers, distinguished according to the VSI-bench method.
- Relative Distance: For a given object, we list other objects in the scene from closest to farthest.
- Room Size: The area of the room in the scene is provided in square meters.
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